CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
暂无分享,去创建一个
[1] Marina De Vos,et al. L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging , 2023, ArXiv.
[2] Jong Wook Kim,et al. Robust Speech Recognition via Large-Scale Weak Supervision , 2022, ICML.
[3] Yeonguk Yu,et al. SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning , 2022, Expert Syst. Appl..
[4] Soumen Pachal,et al. Sequence Prediction under Missing Data: An RNN Approach without Imputation , 2022, CIKM.
[5] Chamira U. S. Edussooriya,et al. Towards Interpretable Sleep Stage Classification Using Cross-Modal Transformers , 2022, ArXiv.
[6] Zirui Wang,et al. CoCa: Contrastive Captioners are Image-Text Foundation Models , 2022, Trans. Mach. Learn. Res..
[7] Xi Peng,et al. Are Multimodal Transformers Robust to Missing Modality? , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Chang Zhou,et al. Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably) , 2022, ICML.
[9] S. Hoi,et al. BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation , 2022, ICML.
[10] Junnan Li,et al. Align before Fuse: Vision and Language Representation Learning with Momentum Distillation , 2021, NeurIPS.
[11] C. Schmid,et al. Attention Bottlenecks for Multimodal Fusion , 2021, NeurIPS.
[12] Alexandre Gramfort,et al. Robust learning from corrupted EEG with dynamic spatial filtering , 2021, NeuroImage.
[13] Maarten De Vos,et al. SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification , 2021, IEEE Transactions on Biomedical Engineering.
[14] Cuntai Guan,et al. An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG , 2021, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[15] C. Igel,et al. U-Sleep: resilient high-frequency sleep staging , 2021, npj Digital Medicine.
[16] Ilya Sutskever,et al. Learning Transferable Visual Models From Natural Language Supervision , 2021, ICML.
[17] Fuchun Sun,et al. Deep Multimodal Fusion by Channel Exchanging , 2020, NeurIPS.
[18] Maarten De Vos,et al. XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Jing Wang,et al. GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification , 2020, IJCAI.
[20] Akara Supratak,et al. TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
[21] Armand Comas Massague,et al. Learning Disentangled Representations of Video with Missing Data , 2020, NeurIPS.
[22] K. Melehan,et al. An Australasian Commentary on the AASM Manual for the Scoring of Sleep and Associated Events , 2020, Sleep and Biological Rhythms.
[23] Ruslan Salakhutdinov,et al. Multimodal Transformer for Unaligned Multimodal Language Sequences , 2019, ACL.
[24] Yan Shen,et al. Brain Tumor Segmentation on MRI with Missing Modalities , 2019, IPMI.
[25] Oliver Y. Chén,et al. SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[26] Andreas Dengel,et al. Overcoming Missing and Incomplete Modalities with Generative Adversarial Networks for Building Footprint Segmentation , 2018, 2018 International Conference on Content-Based Multimedia Indexing (CBMI).
[27] J. Leszek,et al. Sleep Disorders Associated With Alzheimer's Disease: A Perspective , 2018, Front. Neurosci..
[28] Guo-Qiang Zhang,et al. The National Sleep Research Resource: towards a sleep data commons , 2018, BCB.
[29] Wei Cao,et al. BRITS: Bidirectional Recurrent Imputation for Time Series , 2018, NeurIPS.
[30] Ashish Vaswani,et al. Self-Attention with Relative Position Representations , 2018, NAACL.
[31] Abdulhamit Subasi,et al. Ensemble SVM Method for Automatic Sleep Stage Classification , 2018, IEEE Transactions on Instrumentation and Measurement.
[32] Jinsung Yoon,et al. Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks , 2017, IEEE Transactions on Biomedical Engineering.
[33] Graham W. Taylor,et al. Deep Multimodal Learning: A Survey on Recent Advances and Trends , 2017, IEEE Signal Processing Magazine.
[34] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[35] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[36] Louis-Philippe Morency,et al. Multimodal Machine Learning: A Survey and Taxonomy , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Michael Labanowski,et al. Physiology, Sleep Stages , 2017 .
[38] Chao Wu,et al. DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[39] Alexandre Gramfort,et al. Autoreject: Automated artifact rejection for MEG and EEG data , 2016, NeuroImage.
[40] Daniel Cremers,et al. FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture , 2016, ACCV.
[41] Yike Guo,et al. Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks , 2016, ArXiv.
[42] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[43] Geoffrey E. Hinton,et al. Layer Normalization , 2016, ArXiv.
[44] Jiasen Lu,et al. Hierarchical Question-Image Co-Attention for Visual Question Answering , 2016, NIPS.
[45] Christian Jutten,et al. Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects , 2015, Proceedings of the IEEE.
[46] Kyungmin Su,et al. The PREP pipeline: standardized preprocessing for large-scale EEG analysis , 2015, Front. Neuroinform..
[47] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[48] Ram Bilas Pachori,et al. Automatic classification of sleep stages based on the time-frequency image of EEG signals , 2013, Comput. Methods Programs Biomed..
[49] Natheer Khasawneh,et al. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier , 2012, Comput. Methods Programs Biomed..
[50] Kon Max Wong,et al. Electroencephalogram signals classification for sleep-state decision - a Riemannian geometry approach , 2012, IET Signal Process..
[51] Peter Bühlmann,et al. MissForest - non-parametric missing value imputation for mixed-type data , 2011, Bioinform..
[52] Patrick Royston,et al. Multiple imputation using chained equations: Issues and guidance for practice , 2011, Statistics in medicine.
[53] Mohan S. Kankanhalli,et al. Multimodal fusion for multimedia analysis: a survey , 2010, Multimedia Systems.
[54] R. B. Reilly,et al. FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection , 2010, Journal of Neuroscience Methods.
[55] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[56] J. Samet,et al. The Sleep Heart Health Study: design, rationale, and methods. , 1997, Sleep.
[57] E. Wolpert. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .
[58] S. F. Buck. A Method of Estimation of Missing Values in Multivariate Data Suitable for Use with an Electronic Computer , 1960 .
[59] Eleftherios Kofidis,et al. Coupled tensor decompositions for data fusion , 2022, Tensors for Data Processing.
[60] Woonghee Lee,et al. Contextual Imputation with Missing Sequence of EEG Signals Using Generative Adversarial Networks , 2021, IEEE Access.
[61] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[62] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[63] D. Rubin,et al. MULTIPLE IMPUTATIONS IN SAMPLE SURVEYS-A PHENOMENOLOGICAL BAYESIAN APPROACH TO NONRESPONSE , 2002 .